The CLAM statistical approach for classifying generalists and specialists in two distinct habitats is described in Chazdon et al. (2011).
clamtest(comm, groups, coverage.limit = 10, specialization = 2/3,
npoints = 20, alpha = 0.05/20)
# S3 method for clamtest
summary(object, ...)
# S3 method for clamtest
plot(x, xlab, ylab, main, pch = 21:24, col.points = 1:4,
col.lines = 2:4, lty = 1:3, position = "bottomright", ...)A data frame (with class attribute "clamtest"),
with columns:
Species: species name (column names from comm),
Total_*A*: total count in habitat A,
Total_*B*: total count in habitat B,
Classes: species classification, a factor with
levels Generalist, Specialist_*A*,
Specialist_*B*, and Too_rare.
*A* and *B* are placeholders for habitat names/labels found in the
data.
The summary method returns descriptive statistics of the results.
The plot method returns values invisibly and produces a bivariate
scatterplot of species total abundances in the two habitats. Symbols and
boundary lines are shown for species groups.
Community matrix, consisting of counts.
A vector identifying the two habitats. Must have exactly
two unique values or levels. Habitat IDs in the grouping vector
must match corresponding rows in the community matrix comm.
Integer, the sample coverage based correction
is applied to rare species with counts below this limit.
Sample coverage is calculated separately
for the two habitats. Sample relative abundances are used for species
with higher than or equal to coverage.limit total counts per habitat.
Numeric, specialization threshold value between 0 and 1. The value of \(2/3\) represents ‘supermajority’ rule, while a value of \(1/2\) represents a ‘simple majority’ rule to assign shared species as habitat specialists.
Integer, number of points used to determine the boundary lines in the plots.
Numeric, nominal significance level for individual tests. The default value reduces the conventional limit of \(0.05\) to account for overdispersion and multiple testing for several species simultaneously. However, the is no firm reason for exactly this limit.
Fitted model object of class "clamtest".
Labels for the plot axes.
Main title of the plot.
Symbols and colors used in plotting species groups.
Line types and colors for boundary lines in plot to separate species groups.
Position of figure legend, see legend for specification details.
Legend not shown if position = NULL.
Additional arguments passed to methods.
Peter Solymos solymos@ualberta.ca
The method uses a multinomial model based on estimated
species relative abundance in two habitats (A, B). It minimizes bias
due to differences in sampling intensities between two habitat types
as well as bias due to insufficient sampling within each
habitat. The method permits a robust statistical classification of
habitat specialists and generalists, without excluding rare species
a priori (Chazdon et al. 2011). Based on a user-defined
specialization threshold, the model classifies species into
one of four groups: (1) generalists; (2) habitat A specialists; (3)
habitat B specialists; and (4) too rare to classify with confidence.
Chazdon, R. L., Chao, A., Colwell, R. K., Lin, S.-Y., Norden, N., Letcher, S. G., Clark, D. B., Finegan, B. and Arroyo J. P.(2011). A novel statistical method for classifying habitat generalists and specialists. Ecology 92, 1332--1343.
data(mite)
data(mite.env)
sol <- with(mite.env, clamtest(mite, Shrub=="None", alpha=0.005))
summary(sol)
head(sol)
plot(sol)
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